Rethinking network management : models, data-mining and self-learning

Network Service Providers are struggling to re- duce cost and still improve customer satisfaction. We have looked at three underlying challenges to achieve these goals; an overwhelming flow of low-quality alarms, understanding the structure and quality of the delivered services, and automation of service configuration. This thesis proposes solutions in these areas based on domain-specific languages, data-mining and self- learning. Most of the solutions have been validated based on data from a large service provider.We look at how domain-models can be used to capture explicit knowledge for alarms and services. In addition, we apply data- mining and self-learning techniques to capture tacit knowledge. The validation shows that models improve the quality of alarm and service models, and automatically render functions like root cause correlation, service and SLA status, as well as service configuration automation.The data-mining and self-learning solutions show that we can learn from available decisions made by experts and automatically assign alarm priorities.Godkänd; 2012; 20111223 (stewal

Histological characterization of orphan transporter MCT14 (SLC16A14) shows abundant expression in mouse CNS and kidney

Background: MCT14 (SLC16A14) is an orphan member of the monocarboxylate transporter (MCT) family, also known as the SLC16 family of secondary active transmembrane transporters. Available expression data for this transporter is limited, and in this paper we aim to characterize MCT14 with respect to tissue distribution and cellular localization in mouse brain. Results: Using qPCR, we found that Slc16a14 mRNA was highly abundant in mouse kidney and moderately in central nervous system, testis, uterus and liver. Using immunohistochemistry and in situ hybridization, we determined that MCT14 was highly expressed in excitatory and inhibitory neurons as well as epithelial cells in the mouse brain. The expression was exclusively localized to the soma of neurons. Furthermore, we showed with our phylogenetic analysis that MCT14 most closely relate to the aromatic amino acid- and thyroid-hormone transporters MCT8 (SLC16A2) and MCT10 (SLC16A10), in addition to the carnitine transporter MCT9 (SLC16A9). Conclusions: We provide here the first histological mapping of MCT14 in the brain and our data are consistent with the hypothesis that MCT14 is a neuronal aromatic-amino-acid transporter